Users of modern e-commerce services are often presented with a myriad of product choices. To enhance the user experience, recommender systems can be used in conjunction with such services to provide personalized recommendations. A recommender system can be content-based, such that it examines features directly associated with users and products, for example a user's age or the release date of a music album; or can be based on collaborative filtering, such that it examines users' past behaviors or feedback to predict how users might act in the future. However, explicit feedback data, such as user-provided ratings, can be difficult to obtain. Additionally, there is increased interest in the use of implicit feedback data, such as click-throughs, which can be collected faster and with greater scale, and without requiring users to provide an explicit indication of sentiment.